A single <i>Microcoleus</i> species causes benthic cyanotoxic blooms worldwide
Bibliographic record
Abstract
Abstract Recently, proliferations of benthic cyanobacteria producing derivatives of anatoxin-a have been reported in rivers all over the world. In three river systems, in New Zealand, the USA, and Canada, a cohesive cluster of Microcoleus strains was responsible for toxin production. Here, we document a similar toxigenic event that occurred at the mouth of the river Areuse in lake Neuchâtel (Switzerland) and caused the death of several dogs. Using 16S RNA-based community analysis, we show that riverine benthic communities are dominated by Oscillatoriales and especially by Microcoleus strains. We correlate the detection of one sequence variant with the presence of anatoxin-a derivatives and use metagenomics to assemble a complete circular genome of the strain. The strain is distinct from the ones isolated in New Zealand, the USA, and Canada, but belongs to the same species; it shares significant traits with them, in particular a relatively small genome and incomplete vitamin biosynthetic pathways. Overall, our results suggest that the major anatoxin-a-associated benthic proliferations worldwide can be traced back to a single ubiquitous species, Microcoleus anatoxicus, rather than to a diversity of cyanobacterial lineages. We recommend that this species be monitored internationally in order to help predict and mitigate similar cyanotoxic events.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".